{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "2.1.0\nsys.version_info(major=3, minor=6, micro=8, releaselevel='final', serial=0)\nnumpy 1.18.1\npandas 0.23.3\nsklearn 0.21.3\ntensorflow 2.1.0\ntensorflow_core.python.keras.api._v2.keras 2.2.4-tf\nTraining Images ->  453018\nValidation Images ->  78668\n"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import sys\n",
    "import tensorflow as tf\n",
    "import time\n",
    "import random\n",
    "import pathlib\n",
    "\n",
    "from tensorflow import keras\n",
    "\n",
    "print(tf.__version__)\n",
    "print(sys.version_info)\n",
    "for module in np, pd, sklearn, tf, keras:\n",
    "    print(module.__name__, module.__version__)\n",
    "\n",
    "\n",
    "training_path = pathlib.Path('./lol_data_1/training')\n",
    "validation_path = pathlib.Path('./lol_data_1/valid')\n",
    "\n",
    "train_image_paths = list(training_path.glob('*/*'))  \n",
    "valid_image_paths = list(validation_path.glob('*/*'))  \n",
    "\n",
    "train_image_paths = [str(path) for path in train_image_paths]\n",
    "valid_image_paths = [str(path) for path in valid_image_paths]\n",
    "\n",
    "random.shuffle(train_image_paths)\n",
    "random.shuffle(valid_image_paths)\n",
    "train_image_count = len(train_image_paths)\n",
    "valid_image_count = len(valid_image_paths)\n",
    "\n",
    "print(\"Training Images -> \", train_image_count)\n",
    "print(\"Validation Images -> \", valid_image_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "['lol_data_1\\\\training\\\\Braum\\\\4021.jpg',\n 'lol_data_1\\\\training\\\\Vi\\\\61A7BAeaF8 - Copy - Copy - Copy (41) - Copy.png',\n \"lol_data_1\\\\training\\\\Kha'Zix\\\\11635.jpg\",\n 'lol_data_1\\\\training\\\\Brand\\\\273827.jpg',\n 'lol_data_1\\\\training\\\\Jinx\\\\10427.jpg']"
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "train_image_paths[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "['lol_data_1\\\\valid\\\\Riven\\\\14532.jpg',\n 'lol_data_1\\\\valid\\\\Ezreal\\\\548132.jpg',\n 'lol_data_1\\\\valid\\\\Miss Fortune\\\\37433.jpg',\n 'lol_data_1\\\\valid\\\\Teemo\\\\18831.jpg',\n 'lol_data_1\\\\valid\\\\Katarina\\\\59330.jpg']"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "valid_image_paths[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "['Aatrox',\n 'Ahri',\n 'Akali',\n 'Alistar',\n 'Amumu',\n 'Anivia',\n 'Annie',\n 'Aphelios',\n 'Ashe',\n 'Aurelion Sol',\n 'Azir',\n 'Bard',\n 'Blitzcrank',\n 'Brand',\n 'Braum',\n 'Caitlyn',\n 'Camille',\n 'Cassiopeia',\n \"Cho'Gath\",\n 'Corki',\n 'Darius',\n 'Diana',\n 'Dr. Mundo',\n 'Draven',\n 'Ekko',\n 'Elise',\n 'Evelynn',\n 'Ezreal',\n 'Fiddlesticks',\n 'Fiora',\n 'Fizz',\n 'Galio',\n 'Gangplank',\n 'Garen',\n 'Gnar',\n 'Gragas',\n 'Graves',\n 'Hecarim',\n 'Heimerdinger',\n 'Illaoi',\n 'Irelia',\n 'Ivern',\n 'Janna',\n 'Jarvan IV',\n 'Jax',\n 'Jayce',\n 'Jhin',\n 'Jinx',\n \"Kai'Sa\",\n 'Kalista',\n 'Karma',\n 'Karthus',\n 'Kassadin',\n 'Katarina',\n 'Kayle',\n 'Kayn',\n 'Kayn-Blue',\n 'Kayn-Red',\n 'Kennen',\n \"Kha'Zix\",\n 'Kindred',\n 'Kled',\n \"Kog'Maw\",\n 'LeBlanc',\n 'Lee Sin',\n 'Leona',\n 'Lissandra',\n 'Lucian',\n 'Lulu',\n 'Lux',\n 'Malphite',\n 'Malzahar',\n 'Maokai',\n 'Master Yi',\n 'Miss Fortune',\n 'Mordekaiser',\n 'Morgana',\n 'Nami',\n 'Nasus',\n 'Nautilus',\n 'Neeko',\n 'Nidalee',\n 'Noctourne',\n 'Nunu & Willump',\n 'Olaf',\n 'Orianna',\n 'Ornn',\n 'Pantheon',\n 'Poppy',\n 'Pyke',\n 'Qiyana',\n 'Quinn',\n 'Rakan',\n 'Rammus',\n \"Rek'Sai\",\n 'Renekton',\n 'Rengar',\n 'Riven',\n 'Rumble',\n 'Ryze',\n 'Sejuani',\n 'Senna',\n 'Sett',\n 'Shaco',\n 'Shen',\n 'Shyvana',\n 'Singed',\n 'Sion',\n 'Sivir',\n 'Skarner',\n 'Sona',\n 'Soraka',\n 'Swain',\n 'Sylas',\n 'Syndra',\n 'Tahm Kench',\n 'Taliyah',\n 'Talon',\n 'Taric',\n 'Teemo',\n 'Thresh',\n 'Tristana',\n 'Trundle',\n 'Tryndamere',\n 'Twisted Fate',\n 'Twitch',\n 'Udyr',\n 'Urgot',\n 'Varus',\n 'Vayne',\n 'Veigar',\n \"Vel'Koz\",\n 'Vi',\n 'Viktor',\n 'Vladimir',\n 'Volibear',\n 'Warwick',\n 'Wukong',\n 'Xayah',\n 'Xerath',\n 'Xin Zhao',\n 'Yasuo',\n 'Yorick',\n 'Yuumi',\n 'Zac',\n 'Zed',\n 'Ziggs',\n 'Zilean',\n 'Zoe',\n 'Zyra']"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "label_names = sorted(item.name for item in training_path.glob('*/') if item.is_dir())\n",
    "label_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "lol_data_1\\training\\Braum\\4021.jpg  --->   Braum\nlol_data_1\\training\\Vi\\61A7BAeaF8 - Copy - Copy - Copy (41) - Copy.png  --->   Vi\nlol_data_1\\training\\Kha'Zix\\11635.jpg  --->   Kha'Zix\nlol_data_1\\training\\Brand\\273827.jpg  --->   Brand\nlol_data_1\\training\\Jinx\\10427.jpg  --->   Jinx\n"
    }
   ],
   "source": [
    "training_image_labels = [pathlib.Path(path).parent.name for path in train_image_paths]\n",
    "for image, label in zip(train_image_paths[:5], training_image_labels[:5]):\n",
    "    print(image, ' --->  ', label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels_info = []\n",
    "for image_path, label in zip(train_image_paths, training_image_labels):\n",
    "    train_labels_info.append((image_path, label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[('lol_data_1\\\\training\\\\Braum\\\\4021.jpg', 'Braum'),\n ('lol_data_1\\\\training\\\\Vi\\\\61A7BAeaF8 - Copy - Copy - Copy (41) - Copy.png',\n  'Vi'),\n (\"lol_data_1\\\\training\\\\Kha'Zix\\\\11635.jpg\", \"Kha'Zix\"),\n ('lol_data_1\\\\training\\\\Brand\\\\273827.jpg', 'Brand'),\n ('lol_data_1\\\\training\\\\Jinx\\\\10427.jpg', 'Jinx')]\n"
    }
   ],
   "source": [
    "import pprint\n",
    "pprint.pprint(train_labels_info[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "lol_data_1\\valid\\Riven\\14532.jpg  --->   Riven\nlol_data_1\\valid\\Ezreal\\548132.jpg  --->   Ezreal\nlol_data_1\\valid\\Miss Fortune\\37433.jpg  --->   Miss Fortune\nlol_data_1\\valid\\Teemo\\18831.jpg  --->   Teemo\nlol_data_1\\valid\\Katarina\\59330.jpg  --->   Katarina\n"
    }
   ],
   "source": [
    "valid_image_labels = [pathlib.Path(path).parent.name for path in valid_image_paths]\n",
    "for image, label in zip(valid_image_paths[:5], valid_image_labels[:5]):\n",
    "    print(image, ' --->  ', label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_labels_info = []\n",
    "for image_path, label in zip(valid_image_paths, valid_image_labels):\n",
    "    valid_labels_info.append((image_path, label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[('lol_data_1\\\\valid\\\\Riven\\\\14532.jpg', 'Riven'),\n ('lol_data_1\\\\valid\\\\Ezreal\\\\548132.jpg', 'Ezreal'),\n ('lol_data_1\\\\valid\\\\Miss Fortune\\\\37433.jpg', 'Miss Fortune'),\n ('lol_data_1\\\\valid\\\\Teemo\\\\18831.jpg', 'Teemo'),\n ('lol_data_1\\\\valid\\\\Katarina\\\\59330.jpg', 'Katarina')]\n"
    }
   ],
   "source": [
    "pprint.pprint(valid_labels_info[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "filepath    class\n0                 lol_data_1\\training\\Braum\\4021.jpg    Braum\n1  lol_data_1\\training\\Vi\\61A7BAeaF8 - Copy - Cop...       Vi\n2              lol_data_1\\training\\Kha'Zix\\11635.jpg  Kha'Zix\n3               lol_data_1\\training\\Brand\\273827.jpg    Brand\n4                 lol_data_1\\training\\Jinx\\10427.jpg     Jinx\n                                  filepath         class\n0         lol_data_1\\valid\\Riven\\14532.jpg         Riven\n1       lol_data_1\\valid\\Ezreal\\548132.jpg        Ezreal\n2  lol_data_1\\valid\\Miss Fortune\\37433.jpg  Miss Fortune\n3         lol_data_1\\valid\\Teemo\\18831.jpg         Teemo\n4      lol_data_1\\valid\\Katarina\\59330.jpg      Katarina\n"
    }
   ],
   "source": [
    "train_df = pd.DataFrame(train_labels_info)\n",
    "valid_df = pd.DataFrame(valid_labels_info)\n",
    "\n",
    "train_df.columns = valid_df.columns = ['filepath', 'class']\n",
    "\n",
    "print(train_df.head())\n",
    "print(valid_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Found 453018 validated image filenames belonging to 150 classes.\nFound 78668 validated image filenames belonging to 150 classes.\n"
    }
   ],
   "source": [
    "height = 24\n",
    "width = 24\n",
    "channels = 3\n",
    "batch_size = 128\n",
    "num_classes = 150\n",
    "\n",
    "train_datagen = keras.preprocessing.image.ImageDataGenerator(\n",
    "    #像素值 都除以255\n",
    "    rescale = 1./255,\n",
    "    # 图片随机旋转 (5度以内)\n",
    "    rotation_range = 20,\n",
    "    # 图片左右位移  20%限度以内\n",
    "    width_shift_range = 0.2,\n",
    "    # 图片上下位移  20%限度以内\n",
    "    height_shift_range = 0.2,\n",
    "    # 图像剪切强度\n",
    "    shear_range = 0.2,\n",
    "    # 图像缩放强度\n",
    "    zoom_range = 0.2,\n",
    "    # 是否水平翻转\n",
    "    horizontal_flip = False,\n",
    "    # 放大缩小吼， 像素填充方式\n",
    "    fill_mode = 'nearest',\n",
    ")\n",
    "\n",
    "train_generator = train_datagen.flow_from_dataframe(train_df, directory = './',\n",
    "                                                    x_col = 'filepath',\n",
    "                                                    y_col = 'class',\n",
    "                                                    classes = label_names,\n",
    "                                                    target_size = (height, width),\n",
    "                                                    batch_size = batch_size,\n",
    "                                                    seed = 2333,\n",
    "                                                    shuffle = True,\n",
    "                                                    class_mode = \"categorical\")\n",
    "\n",
    "valid_datagen = keras.preprocessing.image.ImageDataGenerator(\n",
    "    rescale = 1./255,\n",
    ")\n",
    "valid_generator = valid_datagen.flow_from_dataframe(valid_df, directory = './',\n",
    "                                                    x_col = 'filepath',\n",
    "                                                    y_col = 'class',\n",
    "                                                    classes = label_names,\n",
    "                                                    target_size = (height, width),\n",
    "                                                    batch_size = batch_size,\n",
    "                                                    seed = 666,\n",
    "                                                    shuffle = True,\n",
    "                                                    class_mode = \"categorical\")\n",
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Training Generator Sample ->  453018\nValidation Generator Sample ->  78668\n"
    }
   ],
   "source": [
    "train_num = train_generator.samples\n",
    "valid_num = valid_generator.samples\n",
    "\n",
    "print(\"Training Generator Sample -> \", train_num)\n",
    "print(\"Validation Generator Sample -> \", valid_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(128, 24, 24, 3) (128, 150)\n[[0. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]\n ...\n [0. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]]\n(128, 24, 24, 3) (128, 150)\n[[0. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]\n ...\n [0. 0. 0. ... 0. 1. 0.]\n [0. 1. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 1. 0.]]\n"
    }
   ],
   "source": [
    "for i in range(2):\n",
    "    x, y = train_generator.next()\n",
    "    print(x.shape, y.shape)\n",
    "    print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Model: \"sequential\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nconv2d (Conv2D)              (None, 24, 24, 16)        448       \n_________________________________________________________________\nconv2d_1 (Conv2D)            (None, 24, 24, 16)        2320      \n_________________________________________________________________\nmax_pooling2d (MaxPooling2D) (None, 12, 12, 16)        0         \n_________________________________________________________________\nconv2d_2 (Conv2D)            (None, 12, 12, 32)        4640      \n_________________________________________________________________\nconv2d_3 (Conv2D)            (None, 12, 12, 32)        9248      \n_________________________________________________________________\nmax_pooling2d_1 (MaxPooling2 (None, 6, 6, 32)          0         \n_________________________________________________________________\nconv2d_4 (Conv2D)            (None, 6, 6, 64)          18496     \n_________________________________________________________________\nconv2d_5 (Conv2D)            (None, 6, 6, 64)          36928     \n_________________________________________________________________\nmax_pooling2d_2 (MaxPooling2 (None, 3, 3, 64)          0         \n_________________________________________________________________\nflatten (Flatten)            (None, 576)               0         \n_________________________________________________________________\ndense (Dense)                (None, 128)               73856     \n_________________________________________________________________\nalpha_dropout (AlphaDropout) (None, 128)               0         \n_________________________________________________________________\ndense_1 (Dense)              (None, 150)               19350     \n=================================================================\nTotal params: 165,286\nTrainable params: 165,286\nNon-trainable params: 0\n_________________________________________________________________\n"
    }
   ],
   "source": [
    "model = keras.models.Sequential([\n",
    "     \n",
    "    keras.layers.Conv2D(filters=16, kernel_size = 3, padding='same',\n",
    "                       activation = 'selu', input_shape = [width, height, channels]),\n",
    "    keras.layers.Conv2D(filters=16, kernel_size = 3, \n",
    "                        padding='same', activation = 'selu'),\n",
    "    keras.layers.MaxPool2D(pool_size=2),\n",
    "    \n",
    "    keras.layers.Conv2D(filters=32, kernel_size = 3, \n",
    "                        padding='same', activation = 'selu'),\n",
    "    keras.layers.Conv2D(filters=32, kernel_size = 3, \n",
    "                        padding='same', activation = 'selu'),\n",
    "    keras.layers.MaxPool2D(pool_size=2),\n",
    "    \n",
    "    keras.layers.Conv2D(filters=64, kernel_size = 3, padding='same',\n",
    "                       activation = 'selu', input_shape = [width, height, channels]),\n",
    "    keras.layers.Conv2D(filters=64, kernel_size = 3, \n",
    "                        padding='same', activation = 'selu'),\n",
    "    keras.layers.MaxPool2D(pool_size=2),\n",
    "    \n",
    "    keras.layers.Flatten(),\n",
    "    keras.layers.Dense(128, activation = 'selu'),\n",
    "    keras.layers.AlphaDropout(rate=0.5),\n",
    "    \n",
    "    keras.layers.Dense(num_classes, activation = 'softmax')\n",
    "])\n",
    "\n",
    "model.compile(loss=\"categorical_crossentropy\",\n",
    "             optimizer = \"adam\", metrics = ['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "logdir = './lol-callbacks'\n",
    "if not os.path.exists(logdir):\n",
    "    os.mkdir(logdir)\n",
    "\n",
    "output_model_file = os.path.join(logdir, \"mini_map_face_recognition.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "W0409 01:52:07.512539  1012 data_adapter.py:1091] sample_weight modes were coerced from\n  ...\n    to  \n  ['...']\nW0409 01:52:07.597312  1012 data_adapter.py:1091] sample_weight modes were coerced from\n  ...\n    to  \n  ['...']\nTrain for 3539 steps, validate for 614 steps\nEpoch 1/20\n3539/3539 [==============================] - 577s 163ms/step - loss: 0.1502 - accuracy: 0.9593 - val_loss: 0.0348 - val_accuracy: 0.9946\nEpoch 2/20\n3539/3539 [==============================] - 384s 108ms/step - loss: 0.1160 - accuracy: 0.9689 - val_loss: 0.0522 - val_accuracy: 0.9931\nEpoch 3/20\n3539/3539 [==============================] - 370s 104ms/step - loss: 0.0999 - accuracy: 0.9740 - val_loss: 0.0484 - val_accuracy: 0.9931\nEpoch 4/20\n3539/3539 [==============================] - 364s 103ms/step - loss: 0.0925 - accuracy: 0.9766 - val_loss: 0.0296 - val_accuracy: 0.9964\nEpoch 5/20\n3539/3539 [==============================] - 371s 105ms/step - loss: 0.0862 - accuracy: 0.9793 - val_loss: 0.0839 - val_accuracy: 0.9917\nEpoch 6/20\n3539/3539 [==============================] - 369s 104ms/step - loss: 0.0841 - accuracy: 0.9803 - val_loss: 0.0220 - val_accuracy: 0.9973\nEpoch 7/20\n3539/3539 [==============================] - 366s 103ms/step - loss: 0.0823 - accuracy: 0.9811 - val_loss: 0.0327 - val_accuracy: 0.9961\nEpoch 8/20\n3539/3539 [==============================] - 365s 103ms/step - loss: 0.0814 - accuracy: 0.9822 - val_loss: 0.0386 - val_accuracy: 0.9957\nEpoch 9/20\n3539/3539 [==============================] - 356s 101ms/step - loss: 0.0803 - accuracy: 0.9829 - val_loss: 0.0232 - val_accuracy: 0.9973\nEpoch 10/20\n3539/3539 [==============================] - 352s 99ms/step - loss: 0.0795 - accuracy: 0.9832 - val_loss: 0.0870 - val_accuracy: 0.9946\nEpoch 11/20\n3539/3539 [==============================] - 355s 100ms/step - loss: 0.0798 - accuracy: 0.9838 - val_loss: 0.1940 - val_accuracy: 0.9860\nEpoch 12/20\n3539/3539 [==============================] - 376s 106ms/step - loss: 0.0787 - accuracy: 0.9842 - val_loss: 0.0241 - val_accuracy: 0.9978\nEpoch 13/20\n3539/3539 [==============================] - 377s 106ms/step - loss: 0.0790 - accuracy: 0.9846 - val_loss: 0.0249 - val_accuracy: 0.9976\nEpoch 14/20\n3539/3539 [==============================] - 369s 104ms/step - loss: 0.0799 - accuracy: 0.9846 - val_loss: 0.0415 - val_accuracy: 0.9969\nEpoch 15/20\n3539/3539 [==============================] - 369s 104ms/step - loss: 0.0809 - accuracy: 0.9851 - val_loss: 0.0631 - val_accuracy: 0.9958\nEpoch 16/20\n3539/3539 [==============================] - 365s 103ms/step - loss: 0.0827 - accuracy: 0.9848 - val_loss: 0.0305 - val_accuracy: 0.9971\nEpoch 17/20\n3539/3539 [==============================] - 353s 100ms/step - loss: 0.0787 - accuracy: 0.9860 - val_loss: 0.0171 - val_accuracy: 0.9983\nEpoch 18/20\n3539/3539 [==============================] - 351s 99ms/step - loss: 0.0835 - accuracy: 0.9855 - val_loss: 0.0729 - val_accuracy: 0.9953\nEpoch 19/20\n3539/3539 [==============================] - 353s 100ms/step - loss: 0.0816 - accuracy: 0.9862 - val_loss: 0.1137 - val_accuracy: 0.9941\nEpoch 20/20\n3539/3539 [==============================] - 351s 99ms/step - loss: 0.0853 - accuracy: 0.9855 - val_loss: 0.0507 - val_accuracy: 0.9973\n"
    }
   ],
   "source": [
    "epochs = 20\n",
    "#因为数据是generator 产生的 所以不能用fit函数\n",
    "history = model.fit_generator(train_generator, steps_per_epoch=train_num // batch_size,\n",
    "                             epochs=epochs, validation_data=valid_generator,\n",
    "                             validation_steps=valid_num//batch_size,\n",
    "                             callbacks = [\n",
    "    keras.callbacks.ModelCheckpoint(output_model_file, monitor='val_loss', mode='min', verbose=0, save_best_only=True)\n",
    "    # ,keras.callbacks.EarlyStopping(patience=5, min_delta=1e-4)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_learning_curves(history, label, epochs, min_value, max_value):\n",
    "    data = {}\n",
    "    data[label] = history.history[label]\n",
    "    data['val_'+label] = history.history['val_' + label]\n",
    "    \n",
    "    pd.DataFrame(data).plot(figsize = (8,5))\n",
    "    plt.grid(True)\n",
    "    plt.axis([0, epochs, min_value, max_value])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot_learning_curves(history, 'accuracy', epochs, 0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot_learning_curves(history, 'loss', epochs, 0, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_and_preprocess_single_img(path):\n",
    "    # read the img through file path\n",
    "    image = tf.io.read_file(path)  \n",
    "    image = tf.image.decode_jpeg(image, channels=3)\n",
    "    # 原始图片大小为(128, 128, 3)，重设为(32, 32)\n",
    "    image = tf.image.resize(image, [24, 24])  \n",
    "    image = tf.cast(image, tf.float32) / 255.0  # 归一化到[0,1]范围\n",
    "    image = np.expand_dims(image, axis = 0) # since you have batch_size, so you need to expand your image\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_single_pic(path, show=False):\n",
    "    \n",
    "    if show:\n",
    "        import matplotlib.image as mpimg\n",
    "        plt.imshow(mpimg.imread(path))\n",
    "    image = load_and_preprocess_single_img(path)\n",
    "    predict_result = model.predict(image)\n",
    "    print(\"This is\", label_names[np.argmax(predict_result, axis=1)[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "This is Riven\n"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "test_pic_path2 = \"./lol_data_1/test/Riven/1935.jpg\"\n",
    "evaluate_single_pic(test_pic_path2, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3.6.8 64-bit",
   "language": "python",
   "name": "python36864bit1184bc59ae474a5b96f7656177dacbec"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}